Active noise control systems are concerned with the reduction of any type of undesirable disturbance or noise signal provided by a noise source through an environment, whether it is borne by electrical, acoustic, vibration, or any other kind of noise media. Since the noise source and environment are often time-varying, the noise signal will often be non-stationary with respect to frequency content, amplitude, and velocity. Active noise control systems control noise by introducing a canceling "anti-noise" signal into the system environment or media through an appropriate secondary source. The anti-noise signal is ideally of equal amplitude and 180 degrees out of phase with the noise signal. Consequently, the combination of the anti-noise signal with the noise signal at an acoustical summing junction results in the cancellation or attenuation of both signals and hence a reduction in noise.
In order to produce a high degree of noise signal attenuation, the amplitude and phase of both the noise and anti-noise signals must match closely as described above. Generally, this is accomplished by an active noise control system using an active noise control system controller that performs digital signal processing. The digital signal processing is performed using one or more adaptive algorithms for adaptive filtering. The adaptive filtering, and more specifically the adaptive algorithms, track all of the changes in the noise signal and the environment in real-time by minimizing an error signal and continuously tracking time variations of the environment. The adaptive filtering may use any of a variety of known and available adaptive algorithms, such as the least-mean-square ("LMS") algorithm, to establish the taps or coefficients of an associated adaptive filter that models the noise source and environment to reduce or minimize the error or residual signal.
Active noise control systems, as compared to passive noise control systems, provide potential benefits such as reduced size, weight, volume, and cost in addition to improvements in noise attenuation. Active noise control is an effective way to attenuate noise that is often difficult and expensive to control using passive means and has application to a wide variety of problems in manufacturing, industrial operations, and consumer products.
Active noise control systems may generally be divided into feedforward active noise control systems and feedback active noise control systems. The present invention will be illustrated as applied to a feedforward active noise control system and thus the present invention will be described in this context.
A feedforward active noise control system generally includes a reference sensor for sensing a noise signal from a noise source and generating a corresponding primary signal in response; an active noise control system controller for generating a secondary signal; a secondary source, located downstream from the reference sensor, for receiving the secondary signal and generating an anti-noise signal to cancel or attenuate the noise signal; and an error sensor for detecting a residual signal and generating a corresponding error signal in response. The residual signal is equivalent to the difference between the noise signal and the anti-noise signal as provided to the error signal through a primary environment. The active noise control system controller receives the primary signal and the error signal and generates the secondary signal in response.
The active noise control system controller is implemented using a digital signal processor and performs digital signal processing using a specific adaptive filter, depending on the type of cancellation scheme employed, for adaptive filtering. Also, the reference sensor, the secondary source, and the error sensor may include interface circuitry for interfacing with the active noise control system controller. The interface circuitry may include analog-to-digital converters, digital-to-analog converters, analog filters such as low pass filters and automatic gain control amplifiers so that signals can be exchanged in the correct domain, i.e., either the digital or analog domain. The interface circuitry may be provided separately.
Feedforward active noise control systems include a primary path that has a transfer function that may be denoted as P(z). The primary path may be defined as the environment from the reference sensor to the error sensor. Feedforward active noise control systems also include a secondary path and a feedback path. The secondary path has a transfer function that may be denoted as S(z). The secondary path may be defined as the environment from the output of the active noise control system controller to the output of the error sensor. This may include interface circuitry such as a digital-to-analog converter, an analog filter, a power amplifier, a loud speaker, an error microphone, and other devices. The feedback path also has a transfer function and may be denoted by F(z). The feedback path may be defined as the environment from the output of the active noise control system controller to the output of the reference sensor. The active noise control system controller, using a digital signal processor, may include an adaptive filter, that is normally denoted by W(z), that attempts to adaptively model the primary path. The objective of the adaptive filter W(z) is to minimize the residual signal or error signal. The adaptive filtering performed by adaptive filter W(z) may be performed either on-line or off-line.
Feedforward active noise control systems suffer from a serious drawback that often harms overall system performance. Whenever the secondary source generates an anti-noise signal to cancel the noise signal, a portion of the anti-noise signal radiates upstream to the reference sensor where it is received along with the noise signal. The path that the anti-noise signal takes when traveling from the secondary source to the reference sensor is the feedback path. The feedback path, once again, may be defined as the media environment from the output of the active noise control system controller to the output of the reference sensor. The portion of the anti-noise signal flowing to the reference sensor along the feedback path is part of a feedback signal that travels through the feedback path. As a consequence of the feedback signal being received at the reference sensor, an incorrect primary signal is provided to the active noise control system controller by the reference sensor and, hence, overall system performance is harmed. If the feedback signal is in phase with the noise signal, the reference sensor will generate a primary signal that is too large. If the feedback signal is out of phase with the noise signal, the reference senor will also generate a signal that is incorrect. In any event, the feedback signal is undesirable and harms overall performance. The feedback signal may also allow the introduction of poles into the response of the system transfer function which results in potential instability if the gain of the feedback loop becomes large.
In certain applications, overall system performance is significantly degraded if the effects of the feedback path are not modeled and neutralized. The modeling of the feedback path and neutralization of the feedback signal becomes especially critical to overall active noise control system performance in applications in which the secondary source is in close proximity or in close communication with the reference sensor. Such systems would include, for example, appliances such as refrigerators and window air conditioner units in which the air ducts are relatively short. In such applications, the secondary source must be located close to the reference sensor by necessity and hence the feedback signal and its adverse effects will be greater.
The feedback path problem has been recognized in the past and several solutions have been proposed with limited success. A first set of proposed solutions has focused on the use, type, and placement of the reference sensors and the secondary sources, while a second set of proposed solutions has focused on signal processing techniques. The first set of proposed solutions involves the use and placement of directional reference sensors and secondary sources to limit or minimize the feedback signal. These proposed solutions add additional expense and complexity to the system and decrease overall reliability while making it difficult, if not impossible, to obtain good directivity over a broad range of frequencies.
The second set of proposed solutions has focused on signal processing techniques and has achieved limited success. The proposed solutions involving signal processing techniques may be generally separated into off-line modeling techniques and on-line modeling techniques. Both off-line modeling and on-line modeling are system identification techniques in which a signal is provided to the system and the resulting signal is analyzed to construct a model of the unknown system. This is accomplished by exciting an unknown path or environment with the known signal and then measuring or analyzing the resulting signal that is provided in response. The present invention involves off-line modeling, and hence, the problems with prior off-line modeling techniques are discussed below.
Off-line feedback path modeling techniques involve providing a known signal in the absence of the noise signal cancellation that is normally provided by the active noise control system. An adaptive algorithm is used to calculate the coefficients or taps of an adaptive filter to minimize the effects of the feedback path. Once the coefficients or taps are established off-line, the taps or coefficients are fixed in a digital filter and are not changed during actual operation of the active noise control system. Although off-line feedback path modeling techniques are adequate in many systems, off-line modeling may not provide adequate performance when used in a system in which parameters are frequently changing. For example, parameters such as temperature and signal flow rate may frequently change resulting in an inaccurate feedback path model because of the changes.
Another problem with prior off-line feedback path modeling techniques is the fact that the noise signal must be eliminated or stopped for the off-line feedback path modeling to correctly and quickly model the unknown environment. This is often not practical in many real-world systems. For example, a power transformer that is energized and used to provide power to customers cannot be easily taken out of service so that off-line modeling may take place. In a system that changes frequently, it may be necessary to routinely perform off-line feedback path modeling to update the fixed digital filter taps or coefficients so that the feedback path remains accurately modeled and active noise control system performance remains accurate. In the event that a noise source cannot be shut off, off-line modeling may proceed if the known signal or modeling signal is provided at a very high amplitude for an extended period of time. In spite of this, the off-line model may still be inaccurate due to the presence of the high amplitude modeling signal. The presence of the high amplitude modeling signal also serves as a source of noise during the time that the extended off-line modeling is performed. This is especially troublesome in acoustical systems.
In addition to the feedback path problem, feedforward active noise control systems also suffer from another serious drawback that also harms overall system performance. As mentioned previously, feedforward active noise control systems also include a secondary path, S(z), that is defined as the environment from the output of the active noise control system controller to the output of the error sensor. As mentioned previously, the secondary path will include interface circuitry and other devices that introduce additional transfer functions into the system which affect overall system operation. The presence of the secondary path transfer function S(z) may result in an unstable system that cannot or will not properly converge. The secondary path, just like the feedback path, is dependent upon environment conditions and is influenced by such parameters as temperature, flow, and other factors. Attempts at solving the secondary path problem have focused on signal processing techniques and have achieved limited success, similar to what was previously mentioned with respect to the feedback path problem. Thus, prior attempts at providing a solution to both the feedback path problem and the secondary path problem have been inadequate.